The impact of investor sentiment on stock liquidity of listed companies in China
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Author(s)Lu XuLink to ORCID Index: https://orcid.org/0009-0008-7170-2161
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Chunxiao XueLink to ORCID Index: https://orcid.org/0000-0002-5914-6974
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Jianing ZhangLink to ORCID Index: https://orcid.org/0000-0003-2964-0733
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DOIhttp://dx.doi.org/10.21511/imfi.21(2).2024.01
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Article InfoVolume 21 2024, Issue #2, pp. 1-14
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Creative Commons Attribution 4.0 International License
Researchers have scrutinized the link between investor sentiment and stock market liquidity globally, yet few have delved into this dynamic in emerging markets, especially China. Utilizing a sample of 1,839 publicly listed companies in China from 2010 to 2019, this study applies firm- and year-fixed-effects models to explore the nexus between investor sentiment and stock illiquidity, employing the Amihud measure for stock illiquidity assessment. The outcomes of these fixed-effect regressions illustrate a significantly positive relationship between investor sentiment and stock liquidity in the Chinese market. The positive link is more evident in scenarios characterized by high firm leverage, rapid revenue growth, larger corporations, greater institutional ownership, higher stock volatility, and lower book-to-market ratios. Intriguingly, this analysis incorporates the quadratic term of investor sentiment to examine the potential for a nonlinear dynamic between stock illiquidity and investor sentiment. The findings elucidate that the effect of investor sentiment on stock liquidity diminishes at elevated levels of sentiment, revealing a nonlinear inverse U-shaped relationship. The positive correlation between investor sentiment and stock liquidity persists across the three divisions of the Chinese Shenzhen Stock Exchange and remains robust using alternative liquidity measures, such as Roll’s impact and zeros impact. Addressing causality concerns, current investor sentiment appears to influence subsequent liquidity levels. These results provide valuable perspectives for policymakers, business executives, and investors in the stock market.
Acknowledgment
This research was funded by the Department of Education of Zhejiang Province General Program [Y202353438], the Wenzhou Association for Science and Technology—Service and Technology Innovation Program [jczc0254], the Wenzhou-Kean University Student Partnering with Faculty Research Program [WKUSPF2023004], and the Wenzhou-Kean University International Collaborative Research Program [ICRP2023002].
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JEL Classification (Paper profile tab)G11, G12, G32, G41
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References38
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Tables9
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Figures0
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- Table 1. Descriptive statistics
- Table 2. Pairwise correlations
- Table 3. Baseline regressions
- Table 4. Nonlinear quadratic regressions
- Table 5. Moderating effects of control variables
- Table 6. Different stock submarkets
- Table 7. Alternative illiquidity measures
- Table 8. Changes in illiquidity and sentiment
- Table 9. Lead illiquidity measure
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